We train our model using SwAV [1]}, and provide a short description of this algorithm here. Given two data augmentations of an image, that we refer to as \(s\)  and \(t\) , we compute their codes \(\mathbf {q}_s\)  and \(\mathbf {q}_t\) .
SwAV trains a network by learning to predict the codes from the other view by minimizing the following loss function:
\(\ell (\mathbf {z}_t, \mathbf {q}_s)+ \ell (\mathbf {z}_s, \mathbf {q}_t),\) 
